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Study on the relationship between catch of Thunnus alalunga and climatic factors based on BP neural network
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Peng Ding1, Xiaorong Zou1, 2, 3, *, Hui Xu4, Shuyi Ding5, Siqi Bai1, Zi Hui Zhang6
Haiyang Xuebao | 2024, 46(9) : 88 - 95
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Haiyang Xuebao | 2024, 46(9): 88-95
• Article •
Study on the relationship between catch of Thunnus alalunga and climatic factors based on BP neural network
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Peng Ding1, Xiaorong Zou1, 2, 3, *, Hui Xu4, Shuyi Ding5, Siqi Bai1, Zi Hui Zhang6
Affiliations
  • 1. College of Marine Living Resource Sciences and Management, Shanghai Ocean University, Shanghai 201306, China
  • 2. Collaborative Innovation Center for National Distant-water Fisheries, Shanghai 201306, China
  • 3. Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China
  • 4. Graduate School of Fisheries Science, Hokkaido University, Hokkaido 0418611, Japan
  • 5. School of Education, Shandong Women’s University, Jinan 250300, China
  • 6. Shandong Lukang Pharmaceutical Co, JiNing 277100, China
Published: 2024-09-01 doi: 10.12284/hyxb2024100
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In order to investigate the impact of climate change on the catch of bigeye tuna, we utilized the annual Pacific bigeye tuna catch data from 1960 to 2021, which was statistically compiled by the Western and Central Pacific Fisheries Commission. We also employed corresponding monthly climate indices, including Niño1+2, Niño3, Niño4, Niño3.4, Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), North Pacific Index (NPI), and global sea-air temperature anomaly (dT). By using a BP neural network and variable sensitivity analysis, we examined the relationship between these low-frequency climate factors and bigeye tuna catch. Our findings revealed that Niño1+2, SOI, NAO, PDO, NPI, and dT are relatively independent climate factors that have an impact on bigeye tuna catch. The optimal lag orders for these climate factors were determined to be 8 years for Niño1+2, 2 years for SOI, 9 years for NAO, 0 years for PDO, 9 years for NPI, and 3 years for dT. Among these factors, Niño1+2, SOI, and NAO were identified as the key climate factors influencing bigeye tuna catch. We constructed an optimal BP neural network model with a structure of 6-8-1, and the ratio of the difference between the predicted and actual bigeye tuna catch to the actual catch has been maintained within 15% since 1971. Additionally, the trend of the predicted and actual catch was found to be basically consistent, indicating a satisfactory level of model fit.

climate change  /  Thunnus alalunga  /  correlation analysis  /  BP neural network model
Peng Ding, Xiaorong Zou, Hui Xu, Shuyi Ding, Siqi Bai, Zi Hui Zhang. Study on the relationship between catch of Thunnus alalunga and climatic factors based on BP neural network[J]. Haiyang Xuebao, 2024 , 46 (9) : 88 -95 . DOI: 10.12284/hyxb2024100
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Year 2024 volume 46 Issue 9
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Article Info
doi: 10.12284/hyxb2024100
  • Receive Date：2023-10-05
  • Online Date：2025-11-26
  • Published：2024-09-01
Article Data
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History
  • Received：2023-10-05
  • Revised：2024-05-09
Funding
Affiliations
    1. College of Marine Living Resource Sciences and Management, Shanghai Ocean University, Shanghai 201306, China
    2. Collaborative Innovation Center for National Distant-water Fisheries, Shanghai 201306, China
    3. Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China
    4. Graduate School of Fisheries Science, Hokkaido University, Hokkaido 0418611, Japan
    5. School of Education, Shandong Women’s University, Jinan 250300, China
    6. Shandong Lukang Pharmaceutical Co, JiNing 277100, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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